Systems and methods for predicting patient health status

ABSTRACT

Systems and methods are provided herein for treating a patient in cardiogenic shock. An intravascular heart pump system is inserted into vasculature of the patient. The heart pump system has a cannula, pump outlet, pump inlet, and rotor. The heart pump system is positioned within the patient such that the cannula extends across the patient&#39;s aortic valve, the pump inlet is located within the patient&#39;s left ventricle, and the pump outlet is located within the patient&#39;s aorta. Data related to time-varying parameters of the heart pump system is acquired from the heart pump system. A plurality of features are extracted from the data. A probability of survival of the patient is determined based on the plurality of features and using a prediction model. The heart pump system is operated to treat the patient.

REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/609,158, filed on Dec. 21, 2017, and entitled“SYSTEMS AND METHODS FOR PREDICTING PATIENT HEALTH STATUS”. The entirecontents of the above-referenced applications are incorporated herein byreference.

BACKGROUND

Acute and chronic cardiovascular conditions reduce quality of life andlife expectancy. A variety of treatment modalities have been developedfor heart health, ranging from pharmaceuticals to mechanical devices andtransplantation. Temporary cardiac support devices, such as heart pumpsystems, provide hemodynamic support, and facilitate heart recovery.Some heart pump systems are percutaneously inserted into the heart andcan run in parallel with the native heart to supplement cardiac output,such as the IMPELLA® family of devices (Abiomed, Inc., Danvers Mass.).

Currently, it is difficult or impossible for clinicians to track apatient's health status. Clinicians tend to rely on qualitativejudgments and indirect estimates of cardiac function to predict apatient's health status, but these processes are inconsistent andunreliable. Determinations of a patient health status may vary betweenclinicians. Furthermore, the process is time-consuming for a clinician,and often a clinician is unable to analyze all of the measurementsassociated with the patient's cardiac function in time to make aninformed health care decision.

SUMMARY

The systems, devices, and methods described herein use predictivemodeling to forecast patient outcome and keep track of patient conditionover time, particularly relating to heart health for patients incardiovascular distress and/or suffering from cardiogenic shock. Inparticular, the systems, devices, and methods enable heart pump systemsto provide data useful for determining a probability of patientsurvival. One way to use the probability of patient survival is to ranka set of patients in order of lowest probability to highest probability,or may be used to assign the set of patients into different tiers fordifferent ranges of probabilities of survival. In this manner, thesystems and methods described herein are a quantitative and objectiveway to allow a clinician to identify the patients in the most direcondition, and direct his/her immediate attention to those patients whomost need it. Another way to use the probability of patient survival totrack an individual patient's probability of survival over a period oftime, to provide a quantitative assessment of that patient's health overtime. In this manner, the systems and methods provide a quantitative andobjective way to allow a clinician to identify whether that patient'shealth is progressing as expected, so that the clinician may update thepatient's treatment plan if needed.

The probability of patient survival may be determined at least based onone or more of a variety of factors including continuous and/or discretemeasurements of heart performance acquired by the heart pump system. Forexample, one data parameter provided by the heart pump system mayinclude cardiac power output (CPO). The CPO value may be used togetherwith one or more clinical data parameters, such as lactate concentrationmeasured from the patient, to determine the probability of survival,which may then be used to alter the operation of the heart pump system.Systems and methods of obtaining CPO and lactate concentration aredescribed in detail below. One way to alter the operation of the heartpump system is to increase or decrease the level of cardiac support fromthe heart pump system, depending on the probability of survival. Forexample, if the probability of survival is high, the patient outlook maybe good, and the heart pump system may decrease the level of cardiacsupport. Alternatively, if the probability of survival is low, thepatient outlook may be worse, and the heart pump system may increase thelevel of cardiac support.

In some aspects, an intravascular heart pump system is inserted intovasculature of the patient. The heart pump system may be inserted usinga minimally invasive procedure. For example, the heart pump system maybe inserted via a catheterization through the femoral artery or vein. Insome implementations, the heart pump system includes a cannula, a pumpinlet, a pump outlet, and a rotor. For example, the intravascular heartpump system may be a percutaneous ventricular assist device, such as theIMPELLA® family of devices (Abiomed, Inc., Danvers Mass.). In someimplementations, the rotor is coupled to a motor. The motor may drivethe rotor and pump blood through the pump. In some implementations, theheart pump system includes one or more sensors. For example, the sensorsmay be configured to acquire data related to the heart pump system'sperformance, heart function, hemodynamic performance, or any othersuitable data. In some implementations, the heart pump system includes acontroller. For example, the heart pump system may include the AutomatedImpella Controller (AIC). The controller may be configured to executeinstructions, analyze data, calculate values, determine relationshipsbetween parameters, or any other suitable task. For example, thecontroller may execute the methods described herein. The controller maycomprise a processor, memory, a user interface, a display screen, atouch screen, user interactive buttons and/or dials, a power source, anyother suitable element, or any combination thereof.

In some implementations, the heart pump system is positioned partiallywithin the patient's heart. In some implementations, the heart pumpsystem is a left ventricular assist device (LVAD). The heart pump systemmay be positioned within the patient such that the cannula extendsacross an aortic valve of the patient, the pump inlet is located withina left ventricle of the patient, and the pump outlet is located withinan aorta of the patient. For example, the heart pump system may beinserted via a catheterization through the femoral artery, into theascending aorta, across the aortic valve and into the left ventricle. Insome implementations, the heart pump system is a right ventricularassist device (RVAD). For example, the heart pump system may be insertedthrough a catheterization procedure through the femoral vein and intothe right atrium. Although some implementations presented herein aredirected to heart pump systems implanted across the aortic valve andresiding partially in the left ventricle, the same concept can beapplied to devices in other regions of the heart, the cardiovascularsystem, or the body.

In some aspects, the systems and methods acquire first data related totime-varying parameters of a heart pump system, extract a plurality offeatures from the first data, and determine a heart health index. Theheart health index may represent the health of the patient heart and maybe indicative of the patient's cardiac performance as well as systemicperfusion leading to overall patient recovery and outcome. In someimplementations, the heart health index represents a value indicative ofa likelihood or probability of survival of the patient.

The systems, devices, and methods presented herein determine a hearthealth index and/or predict patient survival using measurements relatingto a patient's health. In some implementations, the measurements areheart parameters related to cardiac function. In some implementations,the heart pump system takes, measures, processes, or otherwisequantifies the measurements.

The methods described herein may include acquiring first data ormeasurements related to time-varying parameters (such as any of themeasurements described below) of a heart pump system. The first data mayrepresent continuous or near-continuous measurements acquired via theheart pump system, or represent known quantities such as inputs to theheart pump system. The first data relates to operation of or factorsmeasured by the heart pump system, and may include data indicative ofheart rate, pump pressure, differential pressure, motor current,P-level, motor speed, any other data directly provided by, or inferredfrom data directly provided by, the heart pump system, or any suitablecombination thereof. From these measurements, information about heartfunction, and in some cases information about the cardiac assist deviceperformance (such as the occurrence of suction events, for example), canbe determined. This information about heart function can be used in apredictive modeling system to predict patient outcome.

The first data may be determined from measurements obtained by one orsensors on the heart pump system, external systems, or both. Forexample, one or more sensors on the heart pump system may be positionedwithin the patient's heart, outside the patient's heart, or acombination of both, during operation of the heart pump system. In oneexample, sensors on the heart pump system measure pressure within thepatient's vasculature. That pressure may be used in the calculation ofadditional parameters, such as cardiac power output, described below.

The methods described herein may include processing acquired or knowndata, such as the first data described above, to determine or estimateother parameters or features related to patient health or heart pumpoperation. In some implementations, these parameters are determinedbased in part on hysteresis between pressure measurements and motorcurrent measurements that allow the detection of the phase of thecardiac cycle corresponding to a given pair of pressure and currentmeasurements. In some implementations, multiple features are extractedfrom the first data. Extracting the features may include processing thefirst data at the heart pump system or at an external device. These mayinclude left ventricular end diastolic pressure (LVEDP), stroke volume,ejection fraction, chamber distention, chamber hypertrophy, chamberpressure, stroke work, preload state, afterload state, heart rate, heartrecovery, aortic pressure, differential pressure, motor current, motorspeed, pump pressure, left ventricular pressure, end of diastolicpressure, aortic pulse pressure, native cardiac output, cardiac output,CPO, placement, mean flow, target flow, P-level, contractility,relaxation, a placement signal, average placement, standard deviation ofplacement, average placement range, standard deviation of placementrange, average differential pressure, standard deviation of differentialpressure, average differential pressure range, standard deviation ofdifferential pressure range, left ventricular pressure maximum, leftventricular pressure minimum, pump pressure maximum, pump pressure mean,pump pressure minimum, differential pressure maximum, differentialpressure minimum, motor current maximum, motor current minimum, motorcurrent mean, motor speed mean, any other suitable feature related toheart function, and any combination thereof. The first data may beacquired during a first time period during which the heart pump systemis in operation, such as a second, a minute, five minutes, ten minutes,an hour, a few hours, a day, a few days, a week, a month, or anysuitable time frame. The average, mean, and minimum values of thefeatures described above may be the average, mean, or minimum value of afeature during the first time period. The systems and methods describedherein may use these plurality of features to determine a probability ofsurvival or other heart health index of the patient, as described below.

In some implementations, the methods described herein include acquiringsecond data related to physiological parameters of the patient. Thesecond data may be measured from the patient by a clinician or by adevice external to the heart pump system, or may be inferred frommeasurements. The second data may include temperature, weight, height,waist size, body surface area (BSA), age, gender, urine output,creatinine level, potential of Hydrogen (pH), oxygen concentration,carbon dioxide concentration, lactate concentration, or any othersuitable measurement or a patient sample, such as blood, urine, spit,plasma, feces, urine, tissue, or any other suitable sample. For example,a clinician may collect and analyze a blood sample from the patient toobtain the second data. In some implementations, the second data areacquired during the same time period during which the first data areacquired. The heart health index or probability of survival may be basedon the second data.

The second data may be acquired through one or more sensors on the heartpump system and/or through external systems. The one or more sensors onthe heart pump system and/or the external systems may be positionedwithin the patient's heart, outside the patient's heart, or acombination of both. For example, a clinician may measure a lactateconcentration value in a patient's blood then input that lactateconcentration into a user interface on the heart pump system or anothersystem.

In some implementations, the heart pump system itself receives andprocesses both the first data related to cardiac function, as well asthe second data related to physiological parameters. The heart pumpsystem then calculates a heart health index or probability of survivalas described herein. In other implementations, a device separate fromthe heart pump system, such as a computer, mobile device, tablet, or anyother suitable device, receives the first data and the second data, anddetermines the heart health index or probability of survival based onthat data, as described below.

In some implementations, the methods described herein includedetermining a heart health index indicative of the health of a patient'sheart. The heart health index may be indicative of a likelihood ofpatient recovery, comprising a cardiac component and a systemicperfusion component. The cardiac component relates to a patient's hearthealth may include unloading, contractility, or any suitable indicatorof a patient's heart performance. The system perfusion component relatesto a patient's vasculature health and may include cardiac output (CO),aortic pressure mean (AoPm), or any suitable indicator of a patient'scirculatory performance. In some aspects, the heart health index may bea probability of survival of the patient. Probability of survival is avalue that is indicative of a likelihood of patient survival orexpiration. In some implementations, the probability of survival is anumerical value, e.g., between 0 and 1. In some implementations, if theprobability is greater than or equal to a threshold (e.g., 0.5) theprobability of survival indicates survival (e.g., the patient has agreater than 50% chance of living given his or her heart health). Theprobability of survival may be based on the features described above.For example, a patient with low cardiac output, low maximum pressure,high minimum pressure a high standard deviation of differentialpressure, or any suitable combination thereof may have a low probabilityof survival, while a patient with high cardiac output, high maximumpressure, low minimum pressure, a low standard deviation of differentialpressure, or any suitable combination thereof may have a highprobability of survival.

In some aspects, the method includes operating the heart pump system totreat the patient, such as actuating the pump, adjusting a level ofsupport provided by the pump (such as by adjusting the motor speed toincrease or decrease the level of support, for example), or de-actuatingthe pump. For example, if a patient has low CPO and high lactateconcentration, the pump is actuated or turned on, or the level ofsupport may be increased while the patient's health continues to bemonitored. For a patient with high CPO and low lactate concentration, analready operating pump may be de-actuated or turned off, or the level ofsupport may be decreased while continuing to monitor the patient'shealth.

In some implementations, a pump operating parameter value is selectedbased on the probability of survival. A pump operating parameter may beany factor affecting operation of the pump. For example, the pumpoperating parameter may be pump speed, P-level, motor current, targetflow, or any other suitable parameter. In some implementations, pumpspeed is increased based on the heart health index, which may be theprobability of survival (such as if the probability of survival is lowor below some threshold). In some implementations, pump speed isdecreased based on the heart health index (such as if the probability ofsurvival is high or above some threshold).

The heart health index may be determined by using a prediction model.The prediction model may be a machine-learning model. For example, theprediction model may be one of: a logistic regression technique, a deeplearning technique, a decision tree, a random forest technique, a naïveBayes technique, and a support vector machines technique. The hearthealth index may be based on the plurality of features. The method mayfurther include predicting, based on the heart health index, a patientoutcome. In some aspects, the patient outcome may be expiration orsurvival of the patient.

The method may further include displaying the heart health index. Forexample, the heart health index may be displayed using a graphical userinterface on the heart pump system or remotely on another system. Theheart health index may be depicted as a numerical value, colorrepresentation, visual indicator, or any other suitable display method.For example, the AIC may display a green color if the probability ofsurvival for the patient is greater than or equal to a first threshold,display a yellow color if the probability of survival is between a firstthreshold and a second threshold lower than the first threshold, anddisplay a red color if the probability of survival is below or equal tothe second threshold.

The method may further include acquiring a plurality of heart healthindices. The heart health indices may include the heart health index,and each heart health index may correspond to a time period of aplurality of time periods. The method may further include determining,based on the plurality of heart health indices, a change in patienthealth. For example, small changes in a patient factor (e.g., CPO,contractility, motor current mean, etc.) may appear insignificant whenviewed alone, but if viewed in combination with other patient factorsmay show an overall decline in patient health. These multiple factorscan be accounted for in the heart health index. This method ofaggregating multiple patient factors into a single value or trend allowsa patient or clinician to quickly and easily interpret a patient'shealth. The method may further include displaying the plurality of hearthealth indices over the plurality of time periods. For example, theplurality of heart health indices may be displayed using a graphicaluser interface (e.g., on an AIC). For example, a clinician may view agraphical representation of heart health indices over time to easilyvisualize a trend in patient health. In some implementations, if theprobability of survival of the patient is decreasing at a steady rate ordecreasing at a rate above a given threshold, a clinician may be alertedto the patient's declining health. Such notification may include, forexample, an auditory alarm, a flashing light on a user interface, anemail or phone message, or any suitable notification. For example, aclinician may use the heart health index to determine quantitativelythat a patient's probability of survival is decreasing steadily over thecourse of several days (or weeks). This determination would allow theclinician to intervene in the patient's care (such as by adjusting theoperation parameters of the patient's heart pump) to improve thepatient's outlook.

The method may further include displaying an indicator of a relativeimportance of a first feature of the plurality of features compared to asecond feature of the plurality of features. This relative importancemay be shown in a visual display. For example, each feature may be shownas a bar in a bar graph or as a point in a spider plot, with each bar orpoint in the plot given a size or placement relative to its importance.In some implementations, the heart pump system includes a controllerincluding a user interface and a display screen. The relative may bedisplayed on the display screen. In some implementations, a clinicianmay be able to view the indicator remotely, e.g., through a personalcomputer or mobile device. For example, the controller may send aperiodic report on patient status to a clinician, automatically or atthe clinician's request.

In an embodiment, a method for measuring patient health status mayinclude acquiring from a database a training dataset including aplurality of data points relating to time-varying parameters of a heartpump system. For example, the heart pump system's controller or a remotecomputer system may train on data obtained from multiple patient caseswhere patient outcome (e.g., survival or expiration) is known. Themethod may further include pre-processing the dataset to determine aplurality of features corresponding to the plurality of data points andprocessing the plurality of features to determine a pattern. Forexample, training the controller or computer system may includedetermining what patient factors had the greatest and least effect onpatient outcome. The pattern may include a weight of each feature of asubset of the plurality of features. The method may further includeacquiring patient data and calculating, based on the patient data andthe pattern, the heart health index of a patient. By training acontroller or computer system with known case data, the computer systemcan self-correct and “learn” how to accurately predict a patient'sprobability of survival.

In an embodiment, a heart pump system may include a catheter, a motor, arotor operatively coupled to the motor, a pump housing, at least onesensor, and a controller. The pump housing may at least partiallysurround the rotor so that that actuating the motor drives the rotor andpumps blood through the pump housing. The controller may be configuredto perform any of the methods described herein. For example, thecontroller may acquire, during a first time period and from the at leastone sensor, first data related to time-varying parameters of the heartpump system; extract a plurality of features from the first data;determine, using a prediction model and based on the plurality offeatures, a heart health index indicative of the health of the patient'sheart; and predict, based on the heart health index, a patient outcome.

In some aspects, an intravascular heart pump system, such as thatdescribed above or throughout the various embodiments described hereinis inserted into vasculature of the patient. The heart pump system maybe inserted using a minimally invasive procedure. For example, the heartpump system may be inserted via a catheterization through the femoralartery or vein. In some implementations, the heart pump system ispositioned partially within the patient. In some implementations, theheart pump system is a left ventricular assist device (LVAD). The heartpump system may be positioned within the patient such that the cannulaextends across an aortic valve of the patient, the pump inlet is locatedwithin a left ventricle of the patient, and the pump outlet is locatedwithin an aorta of the patient. For example, the heart pump system maybe inserted via a catheterization through the femoral artery, into theascending aorta, across the aortic valve and into the left ventricle. Insome implementations, the heart pump system is a right ventricularassist device (RVAD). For example, the heart pump system may be insertedthrough a catheterization procedure through the femoral vein and intothe right atrium.

In some implementations, the systems and methods described hereinoperate or are configured to operate the heart pump system during afirst time period to provide a first level of cardiac support for thepatient. For example, the heart pump system may operate at a first pumpspeed, P-level, or motor parameter, such as current delivered to themotor, power delivered to the motor, or motor speed. In someimplementations, the system operates to provide a constant or nearconstant level of support to the patient.

In some implementations, the systems and methods described herein obtainat least one CPO value derived from measurements provided by the heartpump system. CPO represents cardiac pumping ability. CPO is a functionof mean arterial pressure and cardiac output, where mean arterialpressure is a function of systolic blood pressure and diastolic bloodpressure and cardiac output is a function of heart rate and strokevolume. Cardiac output can be estimated or measured through a variety ofmeans, such as calculating the area under a volumetric pressure curve ofa heart beat cycle for a patient. In some examples, CPO may be equal tomean arterial pressure multiplied by cardiac output and divided by 451.In some implementations, cardiac power index (CPI) is used instead of orin addition to CPO. CPI represents cardiac pumping ability normalized bybody surface area. In some implementations, CPO is calculated frompressure measurements taken by one or more sensors of the heart pumpsystem. In some implementations, obtaining the at least one CPO valueincludes determining cardiac output over time from sensors of the heartpump system. For example, a controller of the heart pump system maydetermine CPO from systolic, diastolic, and/or differential pressuremeasurements taken during operation of the pump system within thepatient's vasculature. In some implementations, CPO is calculated everytime a pressure measurement is updated at the sensor or the controllerreceives an updated pressure measurement. Alternatively, CPO may becalculated only when an updated pressure measurement is received that isdifferent from a previous measurement by some amount. In someimplementations, CPO is updated regularly at fixed time intervalsfollowing the first time period. For example, the first time intervalmay be 0.01 second, 0.1 second, 0.5 second, 1 second, 5 seconds 10seconds, 1 minute, 10 minutes, 15 minutes, 30 minutes, 1 hour, or anysuitable time interval.

In some implementations, the systems and methods described herein obtainat least one lactate concentration value measured from the patient.Lactate concentration represents the balance between lactate productionand clearance in a patient. Lactate concentration may be measured via apatient's blood. For example, a clinician may measure lactateconcentration by taking blood from a patient. In some implementations,the lactate concentration is manually input by a clinician or other userinto a user interface connected to the heart pump system, or anotherdevice. In some implementations, the lactate concentration is importedvia an electronic wired or wireless connection. For example, lactateconcentrations for a patient may be stored in a remote storage locationthat communicates with the heart pump system to provide physiologicalparameter values for processing. In some implementations, the lactateconcentration value is updated regularly at fixed time intervalsfollowing the first time period. For example, the second time intervalmay be 1 hour, 3 hours, 5 hours, 7 hours, 10 hours, 1 day, 1 week, orany suitable time interval.

In some implementations, the systems and methods described hereindetermine a prediction of patient outcome. The patient outcome may bebased on the at least one CPO value and the at least one lactateconcentration value. In some implementations, the prediction value ofpatient outcome represents a likelihood of patient survival orexpiration. For example, the prediction value of patent outcome may be avalue between zero and one, where one represents a high likelihood ofpatient survival and zero represents a low likelihood of patientsurvival.

In some implementations, the systems and methods described hereinoperate the heart pump system to treat the patient. For example, thepump operation may be altered based on the prediction value of patientoutcome. In particular, altering the pump operation may includeadjusting the operating parameters of the heart pump system to provide asecond level of cardiac support during a second time period followingthe first time period. The second level of cardiac support may be thesame as the first level of cardiac support, or the second level ofcardiac support may be different from the first level of support. In oneexample, adjusting the operating parameters of the heart pump systemincludes adjusting pump speed (such as by increasing or decreasing, forexample) based on a change in cardiac power output, lactateconcentration, or both. It may be desirable to increase pump speed whenthe at least one CPO value is below a first threshold, when the at leastone lactate concentration value is above a second threshold, or both.For example, the first threshold may be a value such as 0.3, 0.4, 0.5,0.6, 0.7, 0.8, 0.9, 1.0 W and the second threshold may be a value suchas 1, 2, 3, 4, 5, 6, 7 mmol/L. A low CPO value and a high lactate valuemay indicate the patient has a relatively low probability of survival.Because the patient is not doing well, the clinician may attempt toincrease the level of cardiac support provided by the pump, byincreasing the pump speed, for example. It may also be desirable todecrease or not change pump speed when the at least one CPO value isabove the first threshold, when the at least one lactate concentrationvalue is below the second threshold, or both. A high CPO value and a lowlactate value may indicate the patient has a relatively high probabilityof survival. Because the patient is doing well, the clinician may decideto not change the parameters of the pump's operation. Alternatively, theclinician may attempt to reduce the level of cardiac support provided bythe pump, or turn off the pump completely.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects and advantages will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows a flowchart of a method for patient condition monitoring;

FIG. 2 shows a flowchart of a method for predicting patient outcome;

FIG. 3 shows a block diagram of log data mining;

FIG. 4 shows two scatter plots with decision boundaries used fortraining and classification;

FIG. 5 shows two scatter plots with decision boundaries used fortraining and classification;

FIG. 6 shows a scatter plot that does not lend itself to decisionboundary separation;

FIG. 7 shows a scatter plot that does not lend itself to decisionboundary separation;

FIG. 8 shows bar graphs ranking feature importance;

FIG. 9 shows a spider plot showing feature rating;

FIG. 10 shows an example of features of a specific patient, Patient X;

FIG. 11 shows an example of features of a specific patient, Patient Y;

FIG. 12 shows characteristics relating to a patient over time through aset of four graphs;

FIG. 13 shows an example of features of a specific patient, Patient Z;

FIG. 14 shows a flowchart of determining a probability of survival of apatient; and

FIG. 15 shows a flowchart of determining a prediction value of patientoutcome based on CPO and lactate concentration.

DETAILED DESCRIPTION

To provide an overall understanding of the systems, method, and devicesdescribed herein, certain illustrative embodiments will be described.Although the embodiments and features described herein are specificallydescribed for use in connection with patient heart health, it will beunderstood that all the components and other features outlined below maybe combined with one another in any suitable manner and may be adaptedand applied to other types of medical therapy and patient health.

The systems, devices, and methods described herein use predictivemodeling to forecast patient outcome and keep track of patient conditionover time, particularly relating to heart health for patients incardiovascular distress and/or suffering from cardiogenic shock. Theforecasted patient outcome may based on a heart health index, which maybe used interchangeably with health index throughout this description.The heart health index may include a cardiac component and a systemicperfusion component, and may be indicative of patient health. Inparticular, the systems, devices, and methods enable heart pump systemsto provide data useful for determining patient outcome, or a probabilityof patient survival. The heart health index may be determined at leastbased on one or more of a variety of factors including continuous and/ordiscrete measurements of heart performance acquired by the heart pumpsystem. For example, one data parameter provided by the heart pumpsystem may include cardiac power output (CPO). The CPO value may be usedtogether with one or more clinical data parameters, such as lactateconcentration, to determine the patient's probability of survival, whichmay then be used to alter the operation of the heart pump system.

The systems and methods described herein also provide a classificationmodel for patient outcome using cardiac parameters. There is an unmetneed in the medical field to monitor patient health status throughpredictive modeling, particularly for patients with heart healthproblems, such as those who have been fitted with a heart pump system.Existing predictive modeling systems generally relate to physiologicaldata but do not take into account the effects of a heart pump systemimplanted in the patient that could affect the patient's health, nor doexisting modeling systems consider the data provided by such implantedheart pump systems. Moreover, these systems do not provide a solutionfor monitoring patient health status over time, or helping cliniciansdetermine which patients most need immediate attention.

The systems and methods described herein may rely on data relating tothe patient's heart pump system operation, such as motor current to thepump, in addition to physiological data measured by the heart pumpsystem or from other sources, to predict patient outcome. Suchphysiological data may include age, gender, body size area (BSA), andclinical values such as lactate concentration, urine output, creatininelevel, pH, concentration of O2, and concentration of CO2. Physiologicaldata and other features used to predict outcome may be manually enteredinto the systems described herein or pulled automatically throughelectronic medical records.

The systems and methods described herein improve a clinician's abilityto quantitatively and objectively determine the heart health of thepatient by incorporating already available data when predicting patientoutcome. By predicting a patient's probability of survival, the systemmay alert a clinician to a patient health problem before it wouldotherwise be detected, thereby providing the clinician with more time totreat the patient than the standard of care. For example, a patient'shealth may be slowly declining but the changes to individual features(e.g., heart rate, CPO, stroke volume, etc.) monitored by the clinicianmay be negligible or subtle enough not to alarm the clinician; the hearthealth index, however, may collate these seemingly insignificant changesand clearly represent an overall decline in patient health. Byrepresenting multiple features within a single metric, the heart healthindex provides an indicator of patient health that the clinician caneasily interpret in an efficient manner. If the heart health index orprobability of survival falls below a threshold or is rapidlydecreasing, a clinician may be notified of the patient's failing healthand may be able to treat the patient in a prompt manner. Moreover, theprediction of a patient's probability of survival may be used to rank aset of patients, or sort the patients into tiers for different ranges ofprobabilities of survival. In this manner, the clinician canquantitatively and objectively identify those patients who most need hisimmediate attention. This is also an improvement over the standard ofcare, in which clinicians may simply do rounds on their patients, in noparticular order.

The operation of the heart pump system may be altered based on the hearthealth index or predicted patient outcome. One way to alter theoperation of the heart pump system is to increase or decrease the levelof cardiac support from the heart pump system, depending on theprobability of survival. For example, if the probability of survival ishigh, the patient outlook may be good, and the heart pump system may beupdated to maintain or decrease the level of cardiac support, or theclinician may even attempt to de-actuate the heart pump. Alternatively,if the probability of survival is low, the patient outlook may be poor,and the heart pump system may increase the level of cardiac support. Theheart pump system may automatically adjust operation of the pump, or aclinician may manually adjust operation of the pump.

In some embodiments, the system trains a machine learning technique(classification model) to fit patient physiological signals with thelabel of patient outcome (survival or expiration). Patient physiologicalsignals may include cardiac parameters such as aorta pressure,differential pressure, left ventricular pressure or any suitable signalderived from a physiological measurement from a patient. Features areextracted from the signals and used in the classification model.Suitable features may include, for example, a statistic of thephysiological signal over a time period, such as a mean or a standarddeviation of the raw signal. The classification model is used toclassify patients with high or low risk and can be used to keep track ofthe patient's condition over time. The classification model may be alogistic regression technique, a deep learning technique, a decisiontree, a random forest technique, a naïve Bayes technique, support vectormachines, or any suitable model. The methods and systems describedherein use the model to predict the patient's health status (e.g., thesurvival probability) using the previous window of signals to representpatient status in real time. Such systems and methods allow a user (suchas a clinician or caregiver, for example) to track patient health statusor health index and view changes to the outcome predicted for a patient,so that “risky” patients (such as patients with decreasing healthindexes) can receive more careful attention. For example, the healthstatus may be displayed on an interface that is connected to or is partof a heart pump system like the Automated Impella Controller (AIC). Rawfeatures may be extracted from heart pump system signals. Such featuresmay include the average or standard deviation of raw signals such asaortic pressure, differential pressure, and left ventricular pressure.Feature engineering can be used to find trends, to find jumps insignals, and to generate signals such as contractility from these rawsignals.

FIG. 1 shows a flowchart of a method 100 for monitoring patientcondition. The method shown in FIG. 1 may determine a heart health indexas described above. At step 102, a system acquires training log (“X”)data for a period of time. The period of time may be one hour (asdepicted in FIG. 1), two hours, one day or any suitable length of time.Log data X may correspond to the most recent period of time of aplurality of periods of time for patient condition monitoring. At step104, the system acquires training patient outcome (“Y”) data. Patientoutcome data associates a patient with survival or expiration. Thepatient outcome data may be associated with the period of time of thelog data, such that the patient outcome data is indicative of thepatient's status at the end of the period of time. Alternatively, thepatient outcome data may not be associated with the period of time ofthe log data, and instead is indicative of the patient's status at atime after the end of the period of time. Log data X and patient outcomedata Y are obtained from a large number N of patients, where N is largeenough to adequately train a model for accurate prediction. Log data wasmeasured and/or aggregated by one or more heart pump systems. The heartpump systems may be at least partially inserted within the heart of theN patients. For example, a heart pump system may extend across thepatient's aorta into his or her left ventricle. The one or more heartpump systems may be the same or different type of heart pump system.Heart pump systems compatible with the present disclosure are disclosedin U.S. patent application Ser. No. 15/709,080 to Edelman et al. (U.S.Patent Publication No. US 2018/0078159 A1, published Mar. 22, 2018), thecontents of which are hereby incorporated by reference in theirentirety. Generally, any other heart pump system or system for obtainingphysiological data from a patient may be used with the presentdisclosure.

At step 106, the system builds a classification model, which may be amachine learning model. The model is trained on the training log data Xand patient outcome data Y. The model may be stored in a database andmay include mathematical rules for classification of features using alearning technique. A learning technique may be logistic regression,decision tree, deep learning, naïve Bayesian, or any other suitabletechnique.

For example, logistic regression is based on an equation used torepresent the predictive model with coefficients learned from trainingdata. A representation of the model may be stored in the database as aseries of the coefficients, each corresponding to a weight indicative ofa relative importance of a particular feature and can be used tocalculate a probability, such as the probability of survival of apatient. Probability of expiration may be calculated as (1+exp(−x))⁻¹,wherein x is equal to α*Feature_α+β*Feature_β+γ*Feature_γ+ . . . for anynumber of features and associated coefficients.

In another example, decision tree learning uses a decision tree as apredictive model to go from observations about an item to conclusionsabout the item's target value. Tree depth may be a hyper-parameter indecision tree learning. A hyper-parameter is a value that cannot beestimated from data used in the model. Hyper-parameters are often usedto help estimate model parameters and can be tuned for a givenpredictive modeling problem. Precision may be used as a performancemetric of a predictive model. By determining the maximum precision ofthe decision tree through tuning hyper-parameters such as tree depth,the system can provide an optimized machine learning model (such asmachine learning model 106), and therefore better provide a prediction(such as patient outcome at step 110 described below). ReceiverOperating Characteristic (ROC) and Area Under Curve (AUC) may also beused as metrics to compare prediction algorithms. In someimplementations, steps 102, 104, 106, and any combination thereof areoptional. For example, the method may start at step 108 described below.In some implementations, the classification model is updatedperiodically with new patient information. In some implementations, theclassification model is collated, developed by, or run by a systemseparate from the heart pump system. For example, a third party systemmay collate data from multiple different heart pumps and build a machinelearning model. That machine learning model, may in some examples, beused to enable steps 108 through 112.

At step 108, the system acquires new log data for a specific patientover the time period. The specific patient may be one of the N patients,for whom new log data was received, or may be a new patient not includedin the N patients. At step 110, the new log data is input into the modeltrained at step 106, to predict patient outcome for the specificpatient. Patient outcome may be a binary value representing survival orexpiration.

At step 112, the model is used, along with the new log data, to predictthe patient condition as a health index over time. In someimplementations, the health index is displayed for patient monitoring.For example, the health index may be displayed on the heart pump systemor may be viewed through a computer system, mobile device, tablet, orany other suitable device. The health index over time is found through asliding window process. At a first time of a plurality of times, thehealth index is calculated for the specific patient over a time window.The health index is then calculated for the specific patient at a secondtime of the plurality of times, still over the time window. For example,at 2:00 pm the system may calculate the health index of Patient W, usingthe past one hour of log data (1:00 pm to 2:00 pm). At 2:15 pm thesystem may again calculate the health index of Patient W, using the pasthour of log data (1:15 pm to 2:15 pm). As such, the window (the one hourtime period) is “slid” across time in 15-minute increments to provide anupdated, time-varying health index for the patient. The time in betweencalculations (15 minutes in the above example) may be any suitable timeincrement, such as one hour, half an hour, one minute, 20 seconds, etc.The health index may be a heart health indicator, indicative of thehealth of the specific patient's heart or a probability of survival. Insome implementations, the health index is graphed over time anddisplayed to a clinician, so that the clinician may see the trend of thehealth index over time.

In some implementations, the health index includes a cardiac componentand a systemic perfusion component. The health index may be indicativeof overall patient recovery and probability survival (i.e., patientoutcome). The cardiac component may include unloading, contractility, orany suitable indicator of a patient's heart performance. The systemperfusion component may include cardiac output (CO), aortic pressuremean (AoPm), or any suitable indicator of a patient's circulatoryperformance.

FIG. 2 shows a flowchart of a method 200 for predicting patient outcomethrough a method similar to that described in relation to FIG. 1. Steps202, 204, 206, and 208 are identical to steps 102, 104, 106, and 108from FIG. 1, respectively. Steps 202, 204, and 206 are optional; anycombination of steps 202, 204, and 206 may be excluded from the methodsdescribed herein. For example, the model may already be developed or maybe imported from an external system. At step 210, the model is used,along with the new log data to predict the patient condition as a healthindex over time. In some implementations, the health index is displayedfor patient monitoring. The health index may be a heart healthindicator, indicative of the health of the specific patient's heart or aprobability of survival. At step 212, the health index is used topredict patient outcome. Patient outcome may be a binary valuerepresenting survival or expiration. For example, the health index mayrepresent patient survival probability as a value x, where x is between0 and 1 (inclusive). The health index may be used to determine a binaryoutput. For example, if the health index is greater than 0.5 (or anyother suitable threshold), the health index may indicate a patientoutcome of survival, which corresponds to a binary value of one. Thispatient outcome may be displayed for a clinician. In FIG. 1, the systempredicts patient outcome at step 110, then uses a sliding window toprovide patient condition monitoring over time, at step 112. Bycontrast, in FIG. 2, the system uses the machine learning model and logdata to provide patient health condition monitoring over time at step210. The system then uses the last value calculated during patientcondition monitoring (the health index) to provide a prediction ofpatient outcome at step 212.

Data used as the log data X or the patient outcome data Y described inrelation to FIGS. 1 and 2 may be stored in a database system. Thedatabase system may include multiple databases or a single database. Forexample, the database may include a clinical data database, a deviceregistry, and AIC logs. The database system may contain data for overthousands of cases. Each case may correspond to a separate patient ormay correspond to an implantation of a heart pump system. The data mayrepresent different case times and may come from time periods specificto each database within the database system. Features described by thedata stored in the database system (for example, in AIC logs) mayinclude pump type, pressure signal, P-Level, flow of a heart pumpsystem, Impella flow, motor current, alarms, outcome or any othersuitable feature. P-level is the performance level of the heart pumpsystem and relates to flow control of the system. As P-level increases,the flow rate and revolutions per minute associated with the heart pumpsystem increase. Data stored in a database system and the data storescontained therein may be used in training the models described herein.

The prediction modeling systems and methods described herein follow adata science approach by making predictions regarding a multitude offeatures using machine learning. Data science projects start withinputting data into a system. The data is pre-processed and featureengineered. Preprocessing challenges may arise when processing log data102 and patient outcome data 104 of FIG. 1, prior to its use in trainingmachine learning model 106. Challenges may include too many missingvalues and non-trustable data. Non-trustable data may include incorrector incomplete data. An example of non-trustable data is when procedureoutcome is listed as “expired” but the actual outcome at the end ofintensive care unit (ICU) support is listed as “survived,” within thedatabase system. Incorrect data, incomplete data and a high proportionof missing data may each affect the performance of the predictive model.“Bad” data may be labeled as “non-trustable” data, “too many missingvalues” data, or any other type of label that indicates the data isuntrustworthy and should be removed from the training data set or filledin to provide a better prediction of patient outcome and thereforeimprove patient health monitoring. Other examples of pre-processing mayinclude data reformatting, removing unusable features, handlingoutliers, filling in missing values, encoding categorical features,scaling and any suitable step to resolve data issues. Once the raw data,such as log data 102 and patient outcome data 104 of FIG. 1, has beenpre-processed, feature data may be extracted. Examples of feature dataare shown in Table 1.

TABLE 1 Average Mean Std Mean Average Std Average Std Average PlacementPlacement Placement Placement Pdiffmean Pdiffmean PdiffRange Cases #Level Level Range Level Range Level Level Level Level 1 74.1 32.3438377.93333 105.7595 39.44167 44.7196 80.61667 7 84.08333 4.970887 44.7166717.79896 29.90833 9.584054 109.3167 3 93.1 2.278157 55.06667 14.347936.01667 1.650673 84.6 4 63.93333 14.35255 68.5 53.44982 40.56667 24.16199.7 5 83.65957 14.96987 147.1702 180.1325 49.39362 37.03634 149.3404 695.98333 49.22313 78.15 52.57624 32.60833 22.67691 137.25 Std AverageStd Average Std Average Std PdiffRange MeanFlow MeanFlow FlowRangeFlowRange LVPMax LVPMax Cases # Level Level Level Level Level LevelLevel 1 87.38938 135.6833 61.04356 32.13333 27.63902 124.75 183.9011 226.25737 216.1667 51.2852 22.46667 12.8614 134.5833 25.91029 3 8.410707142.3333 3.080404 22.76667 9.733733 132.3 14.84284 4 43.12938 216.516740.08719 15.48333 18.91163 123.1167 67.53495 5 79.82142 198.340459.23458 47.93617 41.5556 225.4681 170.5296 6 65.64618 154.5167 41.4255.18333 31.71251 181.4833 117.1217

In some implementations, feature data is be split into training andcross-validation data, and used to build a machine learning technique.The machine learning technique may be applied to new, unclassified datato make a prediction on the new data, such as predicting a health statusof a patient associated with the new data. The prediction, along withthe feature data, may be further analyzed for visualization.

The systems and methods described in relation to FIGS. 1 and 2 and otherprocesses described herein may use a portion or all of the data held inthe database described above. For example, a machine learning techniquemay be trained using only the data stored in AIC logs.

FIG. 3 shows a data flowchart 300 for AIC log data, such as that held inAIC logs, described above in relation to a database system. Long term(LT) Log Data 302 has, for example, a placement signal. LT maycorrespond to one sample per minute or any other suitable sampling rate.Real time (RT)-Log Data 304 produces features such as a heartbeat rate,using information sampled at a sampling rate higher than the samplingrate corresponding to LT data. RT may correspond to 25 Hz (25 samplesper second) or any other suitable sampling rate. The combination of LTLog Data and RT-Log Data is processed as raw and generated features thatare then input to Log Data Mining 306 as X. IQ Database 310 containspatient outcome, which is also input to Log Data Mining 306 as Y. LogData Mining 306 may be used to extract relevant and/or importantfeatures for use in training a machine learning model, such as machinelearning model 106 of FIG. 1, and may therefore be used to provide amore efficient patient outcome prediction.

First data related to cardiac or heart pump parameters and/or seconddata related to physiological parameters, such as the first data andsecond data described above, may be used to predict a patient outcome.In some implementations, first data and/or second data for a pluralityof patients is used to build a predictive modeling system, such as thatdescribed above in relation to FIGS. 1-3. First data and/or second datamay be included in a modeling training set or for feature extraction asdescribed above in relation to FIG. 3. The methods and systems describedherein may test for any significant separation between two groups ofdata corresponding to patient outcome as a function of one or morepatient parameters. For example, FIGS. 4-7 described below show twodifferent patient parameters graphed against one another to determine ifthe parameters show a separation along patient outcome. Only a fewexamples are described herein, but such a test may be implemented forany number of patient parameters of first data and/or second data. Somedata shown herein show significant separation, as shown in FIGS. 4-5.Paired parameters exhibiting significant separation (e.g., asrepresented by boundaries 412, 422, 532) may be more predictiveregarding patient outcome, than other parameters. Other parametersshowing less significant or no significant separation (e.g., asexhibited in FIGS. 6-7 described below) may be less predictive regardingpatient outcome, than other parameters.

FIGS. 4 and 5 show example feature plots used for training andclassification, for features that lend themselves to decision boundaryseparation. In graphs 410, 420, and 530 unshaded dots represent survivalcases, while shaded dots represent expired cases. In each graph themachine learning result is represented by a linear decision boundarythat is used to separate the shaded and unshaded dots with the besttrade-off, using logistic regression. The dots, as a whole, representpatient outcome training data, such as data Y, described above inrelation to step 104 of FIG. 1. Their placement within graphs 410, 420,and 530 is determined by the associated log data training, such as dataX, described above in relation to step 102 of FIG. 1.

Graph 410 depicts a calculated boundary 412 for mean placement signal(PS), which can also be referred to as mean placement level. Placementsignal may be aortic pressure for Impella CP/2.5 cases or differentialpressure for Impella 5.0/LD cases. The x-axis of graph 410 representsthe average of the mean placement signal (PS_mean), while the y-axisrepresents the standard deviation of PS_mean. For example, an unshadeddot may represent a patient who survived (from patient outcome trainingdata 104). The patient is also associated with a set of mean placementlevel data (from training log data 102). The system may compute anaverage mean placement level and standard deviation of mean placementlevel for that patient and graph an associated unshaded dot,accordingly. Once the dots have been graphed for patients included inthe training data, according to their average mean placement level andstandard deviation of placement level, the machine learning model (suchas machine learning model 106 of FIG. 1) calculates a linear decisionboundary 412. The linear decision boundary may be represented by aseries of a coefficients tied to the training data, as described above.When the system receives new data relating to a new patient (such as newlog data 108 of FIG. 1), the system may determine the average meanplacement and standard deviation of mean placement of the new patient.Depending on where these values “place” the patient's dot in graph 410,a predicted patient outcome may be determined based on the location ofthe dot relative to the decision boundary 412. For example, if thepatient has an average mean placement level of 100 and a standarddeviation of mean placement level of 30, the predicted patient outcomewould be survival, according to graph 410. This is because the patient'sdot would fall on the right side of boundary 410, and is therefore morestrongly associated with patients who survived (unshaded dots). Such acalculation (where a new patient falls in relation to a decisionboundary) may constitute a patient outcome prediction like thatdescribed in above in relation to FIG. 1. Thus the decision boundary mayrepresent a threshold for predicting patient outcome. The decisionboundary may be calculated differently in different machine learninginstances. For example, in some instance decision boundary 412 may beshifted to the right or the left, may have a different slope, or may benon-linear.

Similarly, graph 420 depicts a calculated boundary 422 for meandifferential pressure. The x-axis of graph 420 represents the average ofmean differential pressure, while the y-axis represents the standarddeviation of mean differential pressure.

Graph 530 depicts a calculated boundary 532 for maximum left ventricularpressure (LVP). The x-axis of graph 530 represents the average ofmaximum LVP, while the y-axis represents the standard deviation ofmaximum LVP.

FIGS. 6 and 7 shows two example feature plots that do not lendthemselves to decision boundary separation. In graphs 610, 720 unshadeddots represent survival cases, while shaded dots represent expiredcases. Without a boundary separation, the information distributionsshown by graphs 610, 720 may be less helpful than informationdistributions such as those shown in FIGS. 4 and 5 in predicting patientoutcome, because the system is not provided with a clear boundary line(or equation with set of coefficients) with which to categorize newpatient data. The x-axis of graph 610 represents slope of the linearregression of the PS_mean over time, while the y-axis represents thecoefficient of determination (also known as r-squared or r²) of thelinear regression of PS_mean. The data represented in graph 610 isnon-separable because the shaded and unshaded dots have the samepattern. The x-axis of graph 720 represents Slp(PS_mean), while they-axis represents the coefficient of determination of linear regressionof PS_mean. The data represented by graph 720 is semi-separable becausethe shaded and unshaded dots have different patterns but may be “weak”for separation.

FIG. 8 shows example bar graphs ranking feature importance, as anexample result of the machine learning techniques described herein. Thefeature importance may correspond to coefficients of a logisticregression model used, for example, as the machine learning model 106 ofFIG. 1 to predict patient outcome. A higher coefficient in the model maycorrelate to a higher importance of a feature to overall patient health.Knowing the feature importance may be especially helpful for clinicianswhen determining a method of treatment in response to a decline inpatient heart health (as may be exhibited by patient conditionmonitoring 112 of FIG. 1). Generally, features may include aorticpressure, differential pressure, motor current, left ventricularpressure, end of diastolic pressure, aortic pulse pressure, nativecardiac output, cardiac output, CPO, placement, flow, P-level,contractility, and relaxation. These features may be processed todetermine additional features such as average placement, standarddeviation of placement, average placement range, standard deviation ofplacement range, average differential pressure, standard deviation ofdifferential pressure, average differential pressure range, standarddeviation of differential pressure range, left ventricular pressuremaximum and left ventricular pressure minimum. Importance of thesefeatures may be determined by ranking the features using differentcalculations.

Graph 810 shows feature ranking using F-1. F-1 is a statistical termdefined as 2*precision*recall/(precision+recall). Precision equalsTP/(TP+FP) and recall equals TP/(TP+FN), where in TP represents truepositive, FP represents false positive and FN represents false negative.Graph 820 shows feature ranking using precision. Graph 830 shows featureranking using recall. In all three graphs 810, 820, 830 the mostimportant feature (the feature with highest importance) is average LVPmaximum level, suggesting that this feature is useful in understanding aperson's health status, compared to other features shown in FIG. 8. Ingraphs 810 and 830, average mean placement level is ranked as the secondmost important feature. However, in graph 820 average mean placementlevel is ranked third. The differences in feature rankings betweengraphs 810, 820, 830 show that the different metrics (such as precision,recall, or F-1 score) used to calculate the feature importance canaffect the outcome of what features are deemed most important and aregiven the most weight in the model.

Displaying a visual representation of feature importance may be helpfulto clinicians. A graphical representation may allow a clinician to morequickly or easily interpret feature importance, when compared to anumerical display. Specifically, feature importance may be representedthrough a bar graph as depicted in FIG. 8 or through a spider plot asdepicted in FIG. 9. In some implementations, a visual representationshows the patient's health condition and/or feature importance at asingle point in time or an average over multiple points in time. In someimplementations, the visual representation is updated periodically, atregular intervals, or in real time, such that the visual representationappears to a viewer as a video stream.

FIG. 9 shows an example spider plot 900 showing relative feature ratingfor a patient. The heart function index 902 of the patient is 0.58 inthis case, and is an example of a heart health indicator, as describedabove. The CPO 904 associated with the patient is 0.75. CPO is afunction of mean arterial pressure (MAP) and CO. CPO may be used as apredictor for patient outcome and may be a component of a heart healthindicator. FIG. 9 shows patient features and ratings at a single pointwhere the CPO was 0.75 and the heart function index was 0.58. Thesevalues could be updated over time. In one example, CPO may be atime-varying feature used in calculating the likelihood of patientsurvival.

Spider plot 900 visually displays the relative effect of five featureson the patient's health. Each feature is given a rating, representingthe status of the feature for the patient on a scale of one to five. Insome implementations, the rating is on another scale, such as zero toone, one to ten, one to fifty, one to one hundred, one to one thousand,or any other suitable scale. A left ventricular (LV) contractilityrating of 1 indicates a dP/dt (which may be a ventricular contractilityassessment) max greater than 200 mmHg/sec, a rating of two indicatesgreater than 400 mmHg/sec, a rating of three indicates greater than 600mmHg/sec, a rating of four indicates greater than 600 mmHg/sec, and arating of five indicates greater than 1000 mmHg/sec. A LVEDP rating ofone indicates a deviation by 20 mmHg, a rating of two indicates adeviation of 15 mmHg, a rating of three indicates a deviation of 10mmHg, a rating of four indicates a deviation by 5 mmHg, and a rating offive indicates LVEDP in the target range of 10-15 mmHg, where deviationis measured as the deviation from this target range. An LV relaxationrating of 1 indicates a dP/dt max less than 1000 mmHg/sec, a rating oftwo indicates less than 800 mmHg/sec, a rating of three indicates lessthan 600 mmHg/sec, a rating of four indicates less than 400 mmHg/sec,and a rating of five indicates less than 200 mmHg/sec. An AoPm rating ofone indicates 60 mmHg, a rating of two indicates 70 mmHg, a rating ofthree indicates 80 mmHg, a rating of four indicates 90 mmHg, and arating of five indicates 100 mmHg. A CO rating of one indicates a CO of2 L/min, a rating of two indicates 3 L/min, a rating of three indicates4 L/min, a rating of four indicates 5 L/min, and a rating of fiveindicates 6 L/min, where the measurement of CO is a function of heartbeat and stroke volume. For example, LV relaxation 906 is five, LVEDP908 is five, LV Contractility 910 is three, CO 912 is three, and AoPm914 is two. In this instance, AoPm is low relative to the other featuresand therefore the AoPm of the patient is worse relative to the otherfeatures of the patient. Displaying feature data in this manner, and ona uniform rating scale across features, allows a clinician to quicklyview the patient data and perceive which features may need to beaddressed to improve the overall health of the patient. In thisinstance, a clinician may look at spider plot 904 and decide to firstaddress the patient's AoPm. After addressing the patient's AoPm throughclinical means, a clinician may then observe, through patient conditionmonitoring (step 112 of FIG. 1) and on spider plot 904, updated patientheart health status in time and may track progress of the patient.

The features displayed in spider plot 904 may be weighted due to theirrelative feature importance. For example, CO may have a weighting of0.4, LV contractility may have a weighting of 0.2, LVEDP may have aweighting of 0.3, AoPm may have a rating of 0.2 and LV relaxation mayhave a weighting of 0.1. In this example, though AoPm may still have thelowest un-weighted rating, CO may have the lowest weighted rating,because of its relative importance and low rating. In another example,the features may be weighted equally.

FIG. 10 show an example of features of a specific patient, Patient X.Graph 1010 shows a decision boundary 1012. As described above, anunshaded dot represents survival of a patient, while a shaded dotrepresents expiration of a patient. Dot 1014 represents Patient X, whosurvived. Accordingly, dot 1014 is located to the right of decisionboundary 1012, as described above. Decision boundary 1012 may representa line between when a patient is more likely to survive or expire. Forexample, patients to the right of boundary 1012 may be more likely tosurvive while patients to the left of boundary 1012 may be more likelyto expire. The x-axis of graph 1010 represents the average of meanplacement signal (PS), while the y-axis represents the standarddeviation of mean PS. FIG. 10 shows one example a user interface for theclinician when the clinician interprets the patient's heart health byinvestigating placement signal, motor current, P-level, and likelihoodof survival. Specifically, graph 1010 allows the clinician to see agraphical representation of the patient's likelihood of survival inrelation to a boundary between the standard deviation and average ofplacement signal. For example, graph 1010 allows a clinician tovisualize a distance between the boundary 1012 and data representativeof the patient 1014. That distance from a boundary or separation linemay show a clinician how likely a patient is to survive. For example, apatient far to the right of decision boundary 1012 (e.g., a patient withhigh average placement signal) may be more likely to survive than apatient closer to the decision boundary (e.g., a patient with loweraverage placement signal).

Graph 1020 shows placement signal 1022. The y-axis of graph 1020represents pressure in mmHg. Graph 1030 shows motor current signal 1032.The y-axis of graph 1030 represents motor current in mA. Graph 1040shows P-level 1142. The y-axis of graph 1040 represents P-level. Thex-axes of graphs 1020, 1030, 1040 represent time. Graphs 1020, 1030,1040 are shown for the same time period, at the same time scale, for thesame patient, Patient X, represented by dot 1014. Placement signal 1022,motor current signal 1032, and P-level 1042 may be indicative of firstdata related to time-varying parameters of the heart pump system, asdescribed above. Placement signal 1022, motor current signal 1032, andP-level 1042 may be features of a plurality of features used todetermine a heart health index for a patient. In some aspects, the hearthealth index may be a probability of survival of the patient, which maybe used to predict a patient outcome. In this case, the patient outcome(represented by for 1014) is survival. As shown in graph 1040, P-levelis gradually decreased over time. In one example, a clinician may viewthe Patient X's likelihood of survival to determine when and by how muchto change the P-level. In the example shown in FIG. 10, the P-level isstepped down incrementally, thereby decreasing the speed of the pump.

FIG. 11 shows another example of a specific patient, Patient Y. Graph1110 shows a decision boundary 1112. As described above, an unshaded dotrepresents survival of a patient, while a shaded dot representsexpiration of a patient. Dot 1114 represents Patient Y, who expired. Dot1114 is located across boundary line 1112 from the majority of theunshaded “survival” dots. The x-axis of graph 1110 represents theaverage of mean PS, while the y-axis represents the standard deviationof mean PS.

Graph 1120 shows placement signal 1122. The y-axis of graph 1120represents pressure in mmHg. Graph 1130 shows motor current signal 1132.The y-axis of graph 1130 represents motor current in mA. Graph 1140shows P-level 1142. The y-axis of graph 1140 represents P-level. Thex-axes of graphs 1120, 1130, 1140 represent time. Graphs 1120, 1130,1140 are shown for the same time period, at the same time scale, for thesame patient, Patient Y, represented by dot 1122. Placement signal 1122,motor current signal 1132, and P-level 1142 may be indicative of firstdata related to time-varying parameters of the heart pump system, asdescribed above. Placement signal 1122, motor current signal 1132, andP-level 1142 may be features of a plurality of features used todetermine a heart health index for a patient. In some aspects, the hearthealth index may be a probability of survival of the patient, which maybe used to predict a patient outcome. In this case, the patient outcome(represented by dot 1114) is expiration. Graph 1140 shows the variationof target, minimum, and maximum P-level over time. In someimplementations, a clinician varies target P-level to increase ordecrease operation of the pump to treat the patient. For example, theclinician may increase or decrease target P-level if the patient'sprobability of survival decreases. As one example, graph 1140 shows thetarget P-levels the heart pump system was set to to treat the patient.

The differences between FIG. 10 and FIG. 11 show how measuredcharacteristics representative of a patient, such as placement signal,motor current, and P-level may correlate to a patient's survival forexpiration. For example, the Patient X (represented in FIG. 10)survived, while Patient Y (represented in FIG. 11) expired. The systemsand methods described herein predicted this outcome based on thestandard deviation of PS_mean and the average PS_mean (new log data 108of FIG. 1) combined with the boundary lines 1412, 1112 (machine learningmodel 106 of FIG. 1) which were calculated via training data (log data102 and patient outcome 104 of FIG. 1).

FIG. 12 shows characteristics relating to a patient over time through aset 1200 of four graphs. Three graphs 1210, 1220, 1230 show measuredcharacteristics relating to the patient over the same time frame. Graph1210 shows pump pressure maximum 1212, pump pressure mean 1214, pumppressure minimum 1216, differential pressure maximum 1219, anddifferential pressure minimum 1218. The y-axis of graph 1210 representsmillimeters of mercury (mmHg) while the x-axis represents time. Graph1220 shows motor current maximum 1222, mean 1224, and minimum 1226 for aheart pump system placed in the patient. The y-axis of graph 1220represents milliAmps (mA), while the x-axis represents time at the samescale and over the same time period as the x-axis of graph 1210. Graph1230 shows motor speed (MS) mean 1232. The y-axis of graph 1230represents rotations per minute (rpm), while the x-axis represents timeat the same scale and over the same time period as the x-axes of graphs1210 and 1220. Graph 1240 shows a calculated measure of the patient'sheart health, represented as probability of survival 1242. The y-axis ofgraph 1230 represents the percentage probability of survival of thepatient (defined as health status), while the x-axis represents the sametime period as described above in relation to graphs 1210, 1220, 1230.

Pump pressure, differential pressure, motor current, and motor speed maybe indicative of first data related to time-varying parameters of theheart pump system, as described above. Pump pressure maximum 1212, pumppressure mean 1214, pump pressure minimum 1216, differential pressuremaximum 1219, differential pressure minimum 1218, motor current maximum1222, motor current mean 1224, motor current minimum 1226, and motorspeed mean 1232 may be features of a plurality of features used todetermine a heart health index for a patient. In some aspects, the hearthealth index may be the probability of survival 1242 of the patient,which may be used to predict a patient outcome. The patient outcomeprediction may change over time.

Probability of survival 1242 may be calculated via the methods andsystems described above. The values shown in graphs 1210, 1220, 1230 maybe obtained from a heart pump system. Instead of using only the lasthour to predict the survival probability (H-index), the probability ofsurvival may be calculated on sliding windows from the start to the endof the case of the patient. Such a process allows the system to monitorthe health status of the patient. Probability of survival 1242 isdetermined at least in part, in this instance, by the pump pressure,motor current, and motor speed. For example, pump pressure, motorcurrent, and motor speed all dip in value just after time marker 1000.There is a corresponding dip in probability of survival 1242 at the sametime. Such a dip may be indicative of a decline in heart health of thepatient. By predicting a decline in heart health, the system may alert aclinician to a patient health problem before it may be ordinarilydetermined by the clinician, thereby providing the clinician with moretime to treat the patient.

Results of the predictive modeling system are shown in Table 1. Themodel was tested on 13 shock cases. The shock case data is, in thisinstance, provided by Henry Ford Hospital as a “third-party” testdataset. Data for the 13 cases is shown in Table 2.

TABLE 2 Real Predicted Survival Case # Outcome Outcome ProbabilityComments 1 Expired Expired 0.05 Expired in procedure 2 Expired Survived1 Survived procedure but expired later 3 Survived Expired 0.41 Survived,mean pressure is low 4 Survived Survived 0.96 5 Survived Survived 0.9 6Survived Survived 0.86 7 Survived Survived 0.83 8 Survived Survived 0.879 Survived Survived 0.86 10 Survived Survived 0.62 11 Survived Survived0.84 12 Survived Survived 0.59 13 Survived Expired 0.42 Ecmo case, wethought patient did not make if from looking at logsAnother independent test data set (a subset of the data held in thedatabase system described above) was tested. This testing resulted in anaccuracy of 81.4%, with N (the number of patients' data used fortesting) equal to 80 as shown in Table 3.

TABLE 3 N = 80 Random Model Accuracy 55.8% 81.4% Precision 33.0% 79.1%Sensitivity 33.0% 59.8%

FIG. 13 show features over time for an example case through a set ofgraphs 2000. Three graphs 1310, 1320, 1330 show characteristics relatingto a patient over time. Placement signal graph 1310 has a y-axis showingpressure in millimeters of mercury (mmHg) and an x-axis showing time.Flow graph 1320 has a y-axis showing flow in liters per minute (L/min)and an x-axis showing time. Health index graph 1330 has a y-axis showingheart health index and an x-axis showing time. The time scales of graphs1310, 1320, 1330 are the same, and graphs 1310, 1320, 1330 are placedsuch that the x-axes of the three graphs are aligned.

Placement signal and flow may be indicative of first data related totime-varying parameters of the heart pump system, as described above.Placement signal (shown in graph 1310), mean flow and target flow (shownin graph 1320) may be features of a plurality of features used todetermine a heart health index for a patient. The index shown in graph1330 may be the percentage probability of survival of the patient, whichmay be used to predict a patient outcome. The patient outcome predictionmay change over time.

In the example shown in FIG. 13, a patient Z was found unresponsive athome. Patient Z's spouse administered cardiopulmonary resuscitationuntil emergency services arrived. A heart pump system was placed inPatient Z for 34 hours of support. The x-axis of graphs 1310, 1320, 1330start approximately at the time of placement of the heart pump system.The mean flow 1322 of the heart system was approximately 3 L/min at P-7with good performance. Marker 1340 represents a first point in time,specifically Mar. 12, 2016 at 5:30 am. At marker 2040 Patient Z washemodynamically stable. Marker 1350 represents a second point in time,specifically Mar. 12, 2016 at 5:45 pm. At marker 1350, Patient Z turnedblue. Patient's Z's oxygen saturation (O2 sats) dropped, and Patient Zwent into ventricular tachycardia or ventricular fibrillation (VT/VF).Doctors were unable to resuscitate Patient Z.

The time between markers 1340 and 1350 shows significant disruptions tothe mean flow 1322 of the heart system and placement signal 1312. As canbe seen in graph 1330, the significant disruptions to placement signal1312 and mean flow 1322 results in a dramatic change in Patient Z'sheart health index 1332 between markers 1340 and 1350. A clinician couldview the heart health index and determine the patient is at risk. Priorto marker 1340, graphs 1310 and 1320 are relatively steady (whencompared to the high variation shown between markers 1340 and 1350).Graph 1330, however, shows a gradual but steady decline in patienthealth prior to marker 1340. A clinician could view the decline of theheart health index and determine the patient's health is deteriorating.By viewing the decline of the heart health index, a clinician could haveintervened before the time represented by marker 1340 (i.e., before thepatient's flow and placement signal showed significant disruption). Insome cases, early intervention is highly beneficial to patient healthand is a determining factor in patient survival. In some examples, ifthe heart health index is declining (e.g., as shown in graph 1330 priorto marker 1340), a clinician may be alerted to the patient's decline inhealth so that the clinician may intervene in patient care. The hearthealth index graph 1330 may also be used in post-case analysis after apatient has expired.

FIG. 14 shows a flowchart of determining a probability of survival of apatient. At step 1400, an intravascular heart pump system is insertedinto vasculature of the patient. The heart pump system includes acannula, a pump inlet, a pump outlet and a rotor. In someimplementations, the heart pump system is a left ventricular assistdevice (LVAD). In some implementations, the heart pump system is a rightventricular assist device (RVAD). In some implementations, the cannula,pump inlet, pump outlet, and rotor are optional. At step 1402, the heartpump system is positioned within the patient such that the cannulaextends across an aortic valve of the patient, the pump inlet is locatedwithin a left ventricle of the patient, and the pump outlet is locatedwithin an aorta of the patient. In some implementations, step 1402 isoptional and the heart pump system is simply positioned partially withinthe patient. For example, the heart pump system may be inserted via acatheterization through the femoral artery, into the ascending aorta,across the aortic valve and into the left ventricle or through thefemoral vein and into the right atrium.

At step 1403, first data is acquired. The first data relates totime-varying parameters of the heart pump system. The first data mayrepresent continuous or near-continuous measurements acquired via theheart pump system, or represent known quantities such as inputs to theheart pump system. The first data relates to operation of or factorsmeasured by the heart pump system, i.e., without the heart pump systemthe first data would not be known. The first data may include dataindicative of heart rate, pump pressure, differential pressure, motorcurrent, P-level, and/or motor speed. From these measurements, importantinformation about heart function, and in some cases information aboutthe cardiac assist device performance, including the occurrence ofsuction events, can be determined. This information about heart functioncan be used to predict a probability of patient survival, as describedbelow in relation to step 1408.

In some implementations, one or sensors on the heart pump system acquirethe first data. In some implementations, the first data is acquiredthrough external systems. In some implementations, one or sensors on theheart pump system acquire the first data. The one or more sensors on theheart pump system may be positioned within the patient's heart, outsidethe patient's heart, or a combination of both, during operation of theheart pump system. For example, sensors on the heart pump system maymeasure pressure within the patient's vasculature. The measured pressuremay be used in the calculation of additional parameters, such as cardiacpower output, as described above.

At step 1404, a plurality of features are extracted from the first data.Extracting the features may include processing the first data at theheart pump system or at an external device. The plurality of featuresmay include left ventricular end diastolic pressure (LVEDP), strokevolume, ejection fraction, chamber distention, chamber hypertrophy,chamber pressure, stroke work, preload state, afterload state, heartrate, heart recovery, aortic pressure, differential pressure, motorcurrent, motor speed, pump pressure, left ventricular pressure, end ofdiastolic pressure, aortic pulse pressure, native cardiac output,cardiac output, CPO, placement, mean flow, target flow, P-level,contractility, relaxation, a placement signal, average placement,standard deviation of placement, average placement range, standarddeviation of placement range, average differential pressure, standarddeviation of differential pressure, average differential pressure range,standard deviation of differential pressure range, left ventricularpressure maximum, left ventricular pressure minimum, pump pressuremaximum, pump pressure mean, pump pressure minimum, differentialpressure maximum, differential pressure minimum, motor current maximum,motor current minimum, motor current mean, motor speed mean, any othersuitable feature, and any combination thereof.

In some implementations, the first data are acquired during a first timeperiod during which the heart pump system is in operation, such as asecond, a minute, five minutes, ten minutes, an hour, a few hours, aday, a few days, a week, a month, or any suitable time frame. Theaverage, mean, and minimum values of the features described above may bethe average, mean, or minimum value of a feature during the first timeperiod.

At step 1408, a probability of survival of the patient is determined.Probability of survival is a value that is indicative of a likelihood ofpatient survival or expiration. In some implementations, the probabilityof survival is a numerical value, e.g., between 0 and 1. In someimplementations, if the probability is greater than or equal to athreshold (e.g., 0.5) the probability of survival indicates survival(e.g., the patient has a greater than 50% chance of living given his orher heart health). The probability of survival is based on the pluralityof features extracted in step 1404, described above, and is determinedusing a prediction model. In some implementations, the prediction modelis a machine-learning model. For example, the prediction model may beone of a logistic regression technique, a deep learning technique, adecision tree, a random forest technique, a naïve Bayes technique, asupport vector machines technique, or any other suitable model.

At step 1410, the heart pump system is operated to treat the patient. Insome implementations step 1410 is optional. In some implementations, theheart pump system may operate to provide a constant or near constantlevel of support to the patient. In some implementations, the pumpoperation is altered based on the prediction value of patient outcome.In particular, altering the pump operation may include adjusting theoperating parameters of the heart pump system to provide a level ofsupport different than that provided during the time period in which thefirst data was acquired. In one example, adjusting the operatingparameters of the heart pump system includes adjusting pump speed (suchas by increasing or decreasing, for example) based on a change incardiac power output, lactate concentration, or both. It may bedesirable to increase pump speed when one feature value is below a firstthreshold, and when a second feature value is above a second threshold,or both, as described below in relation to FIG. 15.

FIG. 15 shows a flowchart of determining a prediction value of patientoutcome based on CPO and lactate concentration. Steps 1500 and 1502 arethe same as steps 1400 and 1402 described above. At step 1504, the heartpump system is operated during a first time period to provide a firstlevel of cardiac support for the patient. Examples of providing a levelof cardiac support include operating the pump at a P-level or motorspeed, providing current to the pump motor, turning the pump on,inducing flow through the pump or the patient's heart, or any othersuitable method of support. The level at which to operate the heart pumpsystem may be provided by a controller, for example through a userinstruction entered via a user interface.

At step 1506, at least one CPO value is obtained. The at least one CPOvalue is derived from measurements provided by the heart pump system.Optionally, the at least one CPO value is representative of CPO at atleast one time point within the first time period described above inrelation to step 1504. In some implementations, CPO is updated regularlyat fixed time intervals following the first time period. For example,the first time interval may be 0.01 second, 0.1 second, 0.5 second, 1second, 5 seconds 10 seconds, 1 minute, 10 minutes, 15 minutes, 30minutes, 1 hour, or any suitable time interval. At step 1506, at leastone lactate concentration value is also obtained. The at least onelactate concentration value may be measured from the patient. Forexample, the lactate concentration value may be manually input into theheart pump system or may be retrieved from an external database.

At step 1508, a prediction value of patient outcome is determined. Theprediction value is based at least in part on the at least one cardiacpower output value and the at least one lactate concentration valueacquired in step 1506. It may be desirable to increase pump speed whenthe at least one CPO value is below a first threshold, when the at leastone lactate concentration value is above a second threshold, or both.For example, the first threshold may be a value such as 0.3, 0.4, 0.5,0.6, 0.7, 0.8, 0.9, 1.0 W and the second threshold may be a value suchas 1, 2, 3, 4, 5, 6, 7 mmol/L. A low CPO value and a high lactate valuemay indicate the patient has a relatively low probability of survival.Because the patient is not doing well, the clinician may attempt toincrease the level of cardiac support provided by the pump, byincreasing the pump speed, for example. It may also be desirable todecrease or not change pump speed when the at least one CPO value isabove the first threshold, when the at least one lactate concentrationvalue is below the second threshold, or both. A high CPO value and a lowlactate value may indicate the patient has a relatively high probabilityof survival. Because the patient is doing well, the clinician may decideto not change the parameters of the pump's operation. Alternatively, theclinician may attempt to reduce the level of cardiac support provided bythe pump, or turn off the pump completely. Step 1510 is the same as step1410 described above in relation to FIG. 14.

The foregoing is merely illustrative of the principles of thedisclosure, and the apparatuses can be practiced by other than thedescribed embodiments, which are presented for purposes of illustrationand not of limitation. It is to be understood that the apparatusesdisclosed herein, while shown for use in percutaneous insertion of heartpumps, may be applied to apparatuses in other applications requiringhemostasis.

Variations and modifications will occur to those of skill in the artafter reviewing this disclosure. The disclosed features may beimplemented, in any combination and subcombination (including multipledependent combinations and subcombinations), with one or more otherfeatures described herein. The various features described or illustratedabove, including any components thereof, may be combined or integratedin other systems. Moreover, certain features may be omitted or notimplemented.

The systems and methods described may be implemented locally on a heartpump system or a controller of a heart pump system, such as the AIC. Theheart pump system may include a data processing apparatus. The systemsand methods described herein may be implemented remotely on a separatedata processing apparatus. The separate data processing apparatus may beconnected directly or indirectly to the heart pump system through cloudapplications. The heart pump system may communicate with the separatedata processing apparatus in real-time (or near real-time).

In general, embodiments of the subject matter and the functionaloperations described in this specification can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structures disclosed in this specification and theirstructural equivalents, or in combinations of one or more of them.Embodiments of the subject matter described in this specification can beimplemented as one or more computer program products, i.e., one or moremodules of computer program instructions encoded on a computer readablemedium for execution by, or to control the operation of, data processingapparatus. The computer readable medium can be a machine-readablestorage device, a machine-readable storage substrate, a memory device, acomposition of matter affecting a machine-readable propagated signal, ora combination of one or more of them. The term “data processingapparatus” encompasses all apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus caninclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of one or more of them. Apropagated signal is an artificially generated signal, e.g., amachine-generated electrical, optical, or electromagnetic signal that isgenerated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program may correspond to a file in a filesystem. A program can be stored in a portion of a file that holds otherprograms or data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices.

Examples of changes, substitutions, and alterations are ascertainable byone skilled in the art and could be made without departing from thescope of the information disclosed herein. All references cited hereinare incorporated by reference in their entirety and made part of thisapplication.

EXAMPLE EMBODIMENTS

-   -   1. A method for predicting a patient outcome, the method        comprising:        -   acquiring, during a first time period and from a heart pump            system, first data related to time-varying parameters of the            heart pump system;        -   extracting a plurality of features from the first data;        -   determining, using a prediction model and based on the            plurality of features, a heart health index indicative of            the health of the patient's heart; and        -   predicting, based on the heart health index, a patient            outcome.    -   2. The method of embodiment 1, wherein the heart health index is        further indicative of a likelihood of patient recovery.    -   3. The method of embodiment 1 or 2, wherein the heart health        index is representative of a cardiac component and a systemic        perfusion component.    -   4. The method of any one of embodiments 1-3, wherein the heart        pump system is at least partially inserted within the patient's        heart.    -   5. The method of any one of embodiments 1-4, further comprising:        -   acquiring second data related to physiological parameters of            a patient; and        -   extracting a second plurality of features from the second            data,        -   wherein determining the heart health index is further based            on the second plurality of features.    -   6. The method of embodiment 5, wherein the second plurality of        features includes at least one of: age, gender, body surface        area (BSA), urine output, creatinine level, potential of        Hydrogen (pH), oxygen concentration, carbon dioxide        concentration, and lactate concentration.    -   7. The method of any one of embodiments 1-6, further comprising        displaying the heart health index.    -   8. The method of any one of embodiments 1-7, further comprising:        -   acquiring a plurality of heart health indices including the            heart health index, each heart health index corresponding to            a time period of a plurality of time periods; and        -   determining, based on the plurality of heart health indices,            a change in patient health.    -   9. The method of embodiment 8, further comprising displaying the        plurality of heart health indices over the plurality of time        periods.    -   10. The method of any one of embodiments 1-9, wherein the        plurality of features includes at least one of: aortic pressure,        differential pressure, motor current, motor speed, pump        pressure, left ventricular pressure, end of diastolic pressure,        aortic pulse pressure, native cardiac output, cardiac output,        cardiac power output, placement, flow, P-level, contractility,        relaxation, average placement, standard deviation of placement,        average placement range, standard deviation of placement range,        average differential pressure, standard deviation of        differential pressure, average differential pressure range,        standard deviation of differential pressure range, left        ventricular pressure maximum, and left ventricular pressure        minimum.    -   11. The method of any one of embodiments 1-10, wherein the        prediction model is a machine learning model.    -   12. The method of embodiment 11, wherein the machine learning        model is one of: a logistic regression technique, a deep        learning technique, a decision tree, a random forest technique,        a naïve Bayes technique, and a support vector machines        technique.    -   13. The method of any one of embodiments 1-12, further        comprising:        -   displaying an indicator of a relative importance of a first            feature of the plurality of features compared to a second            feature of the plurality of features.    -   14. The method of any one of embodiments 1-13, wherein        determining the heart health index comprises:        -   acquiring, from a database, a training dataset comprising a            plurality of data points relating to time-varying parameters            of a heart pump system;        -   pre-processing the dataset to determine a third plurality of            features corresponding to the plurality of data points;        -   processing the third plurality of features to determine a            pattern, wherein the pattern comprises a weight of each            feature of a subset of the third plurality of features;        -   acquiring patient data;        -   calculating, based on the patient data and the pattern, the            heart health index of a patient.    -   15. The method of any one of embodiments 1-14, wherein the first        data comprises data indicative of placement signal, motor        current, and P-level over time.    -   16. The method of any one of embodiments 1-15, wherein the        plurality of features includes: placement signal, motor current,        P-level, and the standard deviation of the placement signal.    -   17. The method of any one of embodiments 1-16, wherein the first        data comprises data indicative of pump pressure, differential        pressure, motor current, and motor speed over time.    -   18. The method of any one of embodiments 1-17, wherein the        plurality of features includes: pump pressure maximum, pump        pressure mean, pump pressure minimum, differential pressure        maximum, differential pressure minimum, motor current maximum,        motor current minimum, motor current mean, and motor speed mean.    -   19. The method of any one of embodiments 1-18, wherein the first        data comprises data indicative of placement signal and flow over        time.    -   20. The method of any one of embodiments 1-19, wherein the        plurality of features includes:        -   placement signal, mean flow, and target flow.    -   21. The method of any one of embodiments 1-20, wherein the heart        health index is a probability of survival of the patient.    -   22. The method of any one of embodiments 1-21, wherein the        patient outcome is expiration of the patient.    -   23. The method of any one of embodiments 1-21, wherein the        patient outcome is survival of the patient.    -   24. A heart pump system comprising:        -   a catheter;        -   a motor;        -   a rotor operatively coupled to the motor;        -   a pump housing at least partially surrounding the rotor so            that that actuating the motor drives the rotor and pumps            blood through the pump housing;        -   at least one sensor; and        -   a controller configured to:            -   perform the method of any of embodiments 1-23.    -   25. A system comprising a controller configured to perform the        method of any of embodiments 1-23.

What is claimed is:
 1. A method for treating a patient in cardiogenicshock: inserting an intravascular heart pump system into vasculature ofthe patient, the heart pump system comprising a cannula, a pump inlet, apump outlet, and a rotor; positioning the heart pump system within thepatient, such that the cannula extends across an aortic valve of thepatient, the pump inlet is located within a left ventricle of thepatient, and the pump outlet is located within an aorta of the patient;operating the heart pump system to provide a first level of cardiacsupport for the patient; obtaining at least one cardiac power outputvalue derived from measurements provided by the heart pump system, andat least one lactate concentration value measured from the patient;determining, based at least in part on the at least one cardiac poweroutput value and the at least one lactate concentration value, aprediction value of patient outcome; and operating the heart pump systemto treat the patient.
 2. The method of claim 1, further comprisingadjusting, based on the prediction value of patient outcome, operatingparameters of the heart pump system, to provide a second level ofcardiac support for the patient different from the first level.
 3. Themethod of claim 2, wherein adjusting the operating parameters of theheart pump system includes adjusting pump speed based on a change incardiac output or lactate concentration.
 4. The method of claim 3,wherein adjusting pump speed includes increasing pump speed when the atleast one cardiac power output value is below a first threshold and theat least one lactate concentration value is above a second threshold. 5.The method of claim 3, wherein adjusting pump speed includes decreasingpump speed when the at least one cardiac power output value is above afirst threshold and the at least one lactate concentration value isbelow a second threshold.
 6. The method of claim 1, wherein the heartpump system provides the first level of cardiac support for the patientduring a first time period; and further comprising: updating the atleast one cardiac power output value after a first time intervalfollowing the first time period, and updating the at least one lactateconcentration value after a second time interval following the firsttime period.
 7. The method of claim 1, wherein the at least one lactateconcentration value is measured from the patient by a clinician andprovided over a user interface to the heart pump system.
 8. The methodof claim 1, wherein obtaining the at least one cardiac power outputvalue includes determining cardiac output over time from sensors of theheart pump system.
 9. The method of claim 1, wherein the predictionvalue of patient outcome indicates expiration or survival of thepatient.